Course Content

Data Science Interview Challenge

1. Python

Introduction Challenge 1: List Comprehension Challenge 2: String Manipulation Challenge 3: Dictionary Manipulation Challenge 4: Nested Loops Challenge 5: Classes Theoretical Questions

2. NumPy

Challenge 1: Array Creation Challenge 2: Array Manipulation Challenge 3: Statistical Insights Challenge 4: Handling Missing Values Challenge 5: Subarray Sorting Theoretical Questions

3. Pandas

Challenge 1: DataFrame Creation Challenge 2: Data Grouping Challenge 3: Indexing and MultiIndexing Challenge 4: Altering DataFrame Challenge 5: Iterating Over Data Theoretical Questions

4. Matplotlib

Challenge 1: Fundamentals of Plotting Challenge 2: Other Graph Types Challenge 3: Subplots Challenge 4: Customizing Plots Challenge 5: Adding Text to Plots Theoretical Questions

5. Seaborn

Challenge 1: Visualizing Distributions Challenge 2: Exploring Categorical Data Challenge 3: Relational Plots Challenge 4: Regression Plots Challenge 5: Matrix Plots Theoretical Questions

6. Statistics

Challenge 1: Probabilities and Distributions Challenge 2: Bayes' Theorem Challenge 3: Hypothesis Testing Challenge 4: Confidence Intervals Challenge 5: Correlation Theoretical Questions

7. Scikit-learn

Challenge 1: Data Scaling Challenge 2: Basic Model Creation Challenge 3: Pipelines Challenge 4: Cross-validation Challenge 5: Hyperparameter Tuning Theoretical Questions

Challenge 1: Visualizing Distributions

Understanding how data is distributed is fundamental in the data analysis process. Distributions help us to visualize the central tendencies, variability, and the presence of any outliers in our dataset. Seaborn, a statistical plotting library built on top of Matplotlib, provides a suite of tools that makes visualizing distributions a breeze.

The various plots and tools under Seaborn's distribution utilities can:

Examine the distribution of a dataset.
Visualize the relationship between multiple variables.
Display the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a comprehensive view of the data's distribution and its characteristics.

Task

Using Seaborn, visualize the distribution of a dataset:

Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Task

Using Seaborn, visualize the distribution of a dataset:

Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Everything was clear?

Section 5. Chapter 1

Challenge 1: Visualizing Distributions

The various plots and tools under Seaborn's distribution utilities can:

Examine the distribution of a dataset.
Visualize the relationship between multiple variables.
Display the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a comprehensive view of the data's distribution and its characteristics.

Task

Using Seaborn, visualize the distribution of a dataset:

Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Task

Using Seaborn, visualize the distribution of a dataset:

Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Everything was clear?

Section 5. Chapter 1

Challenge 1: Visualizing Distributions

The various plots and tools under Seaborn's distribution utilities can:

Examine the distribution of a dataset.
Visualize the relationship between multiple variables.
Display the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a comprehensive view of the data's distribution and its characteristics.

Task

Using Seaborn, visualize the distribution of a dataset:

Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Task

Using Seaborn, visualize the distribution of a dataset:

Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Everything was clear?

The various plots and tools under Seaborn's distribution utilities can:

Examine the distribution of a dataset.
Visualize the relationship between multiple variables.
Display the underlying probability distributions of datasets.

Using Seaborn to create distribution plots ensures that the viewer can get a comprehensive view of the data's distribution and its characteristics.

Task

Using Seaborn, visualize the distribution of a dataset:

Plot a univariate distribution of data using a histogram and overlay it with a kernel density estimate (KDE).
Visualize the bivariate distribution between two variables using a scatter plot and include a KDE plot to see the data's density.

Section 5. Chapter 1

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